一种基于分解和协同的高维多目标进化算法

现实中高维多目标优化问题普遍存在,而且其巨大的目标空间使得经典的多目标进化算法面临严峻挑战,提出一种基于分解和协同策略的高维多目标进化算法MaOEA/DCE.该算法利用混合水平正交实验设计方法产生接近于指定规模且均匀分布于聚合系数空间的权重向量,提高种群的分布性;其次,算法将差分进化算子和自适应SBX算子进行协同进化以产生高质量的子代个体,改善算法的收敛性.该算法与另外五种高性能的多目标进化算法在基准测试函数集DTLZ{1,2,4,5}上进行IGD⁺性能指标实验,结果表明MaOEA/DCE在收敛性、多样性和稳定性方面总体具有显著的性能优势.

Many-objective Evolutionary Algorithm Based on Decomposition and Coevolution

In reality, many-objective optimization problems (MaOPs) are ubiquitous, and their huge objective space makes representative multi-objective evolutionary algorithms face severe challenges. A many-objective evolutionary algorithm based on decomposition and coevolution (MaOEA/DCE) is proposed in the paper. The method of mix-level orthogonal experimental design is used to generate an even-distributed set of weight vectors in the space of weight coefficient in order to improve the population diversity. Second, the differential evolution operator is combined with the adaptive SBX operator to generate high-quality offspring, so as to improve the convergence of MaOEA/DCE. The MaOEA/DCE is compared with other five representative multi-objective evolutionary algorithms on the benchmark test set DTLZ{1,2,4,5} based on the performance of IGD+, and the experimental results show that the presented algorithm has more significant performance advantages of convergence, diversity and robustness over the peering algorithms.